Artificial Minds with Consciousness and Common sense Aspects

Shylaja K R, Vijayakumar Maragal Venkatamuni, E. V. Prasad, D. Davis
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引用次数: 2

Abstract

The research work presented in this article investigates and explains the conceptual mechanisms of consciousness and common-sense thinking of animates. These mechanisms are computationally simulated on artificial agents as strategic rules to analyze and compare the performance of agents in critical and dynamic environments. Awareness and attention to specific parameters that affect the performance of agents specify the consciousness level in agents. Common sense is a set of beliefs that are accepted to be true among a group of agents that are engaged in a common purpose, with or without self-experience. The common sense agents are a kind of conscious agents that are given with few common sense assumptions. The so-created environment has attackers with dependency on agents in the survival-food chain. These attackers create a threat mental state in agents that can affect their conscious and common sense behaviors. The agents are built with a multi-layer cognitive architecture COCOCA (Consciousness and Common sense Cognitive Architecture) with five columns and six layers of cognitive processing of each precept of an agent. The conscious agents self-learn strategies for threat management and energy level maintenance. Experimentation conducted in this research work demonstrates animate-level intelligence in their problem-solving capabilities, decision making and reasoning in critical situations.
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具有意识和常识方面的人工智能
本文提出的研究工作调查并解释了动物的意识和常识思维的概念机制。这些机制在人工智能体上进行了计算模拟,作为策略规则来分析和比较智能体在关键和动态环境中的性能。对影响智能体表现的特定参数的意识和注意指定了智能体的意识水平。常识是一组具有共同目的的行为者,不论是否具有自我经验,都接受为真理的信念。常识主体是一种有意识的主体,它被赋予很少的常识假设。在这样创造的环境中,攻击者依赖于生存食物链中的代理。这些攻击者在代理中创造了一种威胁心理状态,可以影响他们的意识和常识行为。智能体采用多层认知架构COCOCA (Consciousness and Common sense cognitive architecture)构建,对智能体的每条规则进行五列六层的认知处理。有意识的智能体自我学习威胁管理和能量水平维持策略。在这项研究工作中进行的实验展示了他们在关键情况下解决问题、决策和推理能力方面的动画级智力。
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